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What is TimesFM-3?

TimesFM-3 is an innovative foundation model for time series analysis, distinguished by its capability to deliver highly accurate multivariate forecasts with a single forward pass. With a substantial framework of 330 million parameters, this model has been pre-trained on an extensive dataset comprising both real-world and synthetic time series information, accumulating over 1 trillion time points, which significantly bolsters its effectiveness and zero-shot generalization compared to its predecessors. It excels at simultaneously forecasting multiple coevolving time series while effectively recognizing dependencies that enhance predictive accuracy without necessitating task-specific fine-tuning. Additionally, it is designed to handle various forecasting objectives, including point and quantile predictions, and takes into account both historical covariates and dynamic covariates related to future scenarios, such as planned promotions, holidays, or shifts in weather. Employing a decoder-only transformer architecture, TimesFM-3 adeptly processes sequential data in chunks of 32 time steps, utilizing alternating causal temporal attention and full variate attention to seamlessly weave together patterns across time and interconnected series. As a result, this model serves as a powerful resource for forecasting intricate, time-dependent phenomena across diverse applications, making it a significant advancement in the field of time series forecasting. Its versatility and precision open up new avenues for exploration and application in various domains.

What is Covariant?

Revolutionize your warehouse operations and meet scalability challenges with state-of-the-art robotic automation, pioneered by leading AI experts across the globe. The Covariant Brain, which has learned from countless selections in a variety of warehouse environments, empowers robots to autonomously and adeptly pick nearly any SKU or product right from the outset. Furthermore, Covariant's AI-driven Robotic Putwall enhances this capability by automatically sorting items from mixed-SKU containers, effectively addressing labor shortages while increasing overall productivity. Each solution is meticulously tailored to fit seamlessly with your existing floor layout, systems, and workflows both upstream and downstream. Moreover, the fleet learning feature enables each robot to gain insights from the shared experiences of interconnected robots operating in warehouses around the world. Consequently, once deployed, Covariant robots are primed to handle nearly any SKU or item, regardless of its dimensions or packaging type. This innovative technology not only simplifies operations but also leads to remarkable improvements in efficiency and productivity within your warehouse setting. As a result, businesses can expect to see a significant return on investment as they embrace these advancements.

Media

Media

Integrations Supported

Additional information not provided

Integrations Supported

Additional information not provided

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Online Training

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

research.google/blog/timesfm-3-a-zero-shot-foundation-model-for-multivariate-forecasting/

Company Facts

Organization Name

Covariant

Date Founded

2017

Company Location

United States

Company Website

covariant.ai/

Categories and Features

AI Models

Not specified

Foundation Models

Not specified

Categories and Features

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